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 },
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Example 2.3: The U.S. Gasoline Market"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look at example 2.3 (p. 17) of Greene's [Econometric Analysis](http://people.stern.nyu.edu/wgreene/Text/econometricanalysis.htm).  We're estimating some elasticities using linear regresion in a log-log model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import statsmodels.api as sm\n",
      "import statsmodels.formula.api as smf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gas = pd.read_csv('http://www.stern.nyu.edu/~wgreene/Text/'\n",
      "                  'Edition7/TableF2-2.csv', index_col='YEAR')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gas.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>GASEXP</th>\n",
        "      <th>POP</th>\n",
        "      <th>GASP</th>\n",
        "      <th>INCOME</th>\n",
        "      <th>PNC</th>\n",
        "      <th>PUC</th>\n",
        "      <th>PPT</th>\n",
        "      <th>PD</th>\n",
        "      <th>PN</th>\n",
        "      <th>PS </th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YEAR</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1953</th>\n",
        "      <td>  7.4</td>\n",
        "      <td> 159565</td>\n",
        "      <td> 16.668</td>\n",
        "      <td> 8883</td>\n",
        "      <td> 47.2</td>\n",
        "      <td> 26.7</td>\n",
        "      <td> 16.8</td>\n",
        "      <td> 37.7</td>\n",
        "      <td> 29.7</td>\n",
        "      <td> 19.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1954</th>\n",
        "      <td>  7.8</td>\n",
        "      <td> 162391</td>\n",
        "      <td> 17.029</td>\n",
        "      <td> 8685</td>\n",
        "      <td> 46.5</td>\n",
        "      <td> 22.7</td>\n",
        "      <td> 18.0</td>\n",
        "      <td> 36.8</td>\n",
        "      <td> 29.7</td>\n",
        "      <td> 20.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1955</th>\n",
        "      <td>  8.6</td>\n",
        "      <td> 165275</td>\n",
        "      <td> 17.210</td>\n",
        "      <td> 9137</td>\n",
        "      <td> 44.8</td>\n",
        "      <td> 21.5</td>\n",
        "      <td> 18.5</td>\n",
        "      <td> 36.1</td>\n",
        "      <td> 29.5</td>\n",
        "      <td> 20.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1956</th>\n",
        "      <td>  9.4</td>\n",
        "      <td> 168221</td>\n",
        "      <td> 17.729</td>\n",
        "      <td> 9436</td>\n",
        "      <td> 46.1</td>\n",
        "      <td> 20.7</td>\n",
        "      <td> 19.2</td>\n",
        "      <td> 36.1</td>\n",
        "      <td> 29.9</td>\n",
        "      <td> 20.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1957</th>\n",
        "      <td> 10.2</td>\n",
        "      <td> 171274</td>\n",
        "      <td> 18.497</td>\n",
        "      <td> 9534</td>\n",
        "      <td> 48.5</td>\n",
        "      <td> 23.2</td>\n",
        "      <td> 19.9</td>\n",
        "      <td> 37.2</td>\n",
        "      <td> 30.9</td>\n",
        "      <td> 21.8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "      GASEXP     POP    GASP  INCOME   PNC   PUC   PPT    PD    PN   PS \n",
        "YEAR                                                                    \n",
        "1953     7.4  159565  16.668    8883  47.2  26.7  16.8  37.7  29.7  19.4\n",
        "1954     7.8  162391  17.029    8685  46.5  22.7  18.0  36.8  29.7  20.0\n",
        "1955     8.6  165275  17.210    9137  44.8  21.5  18.5  36.1  29.5  20.4\n",
        "1956     9.4  168221  17.729    9436  46.1  20.7  19.2  36.1  29.9  20.9\n",
        "1957    10.2  171274  18.497    9534  48.5  23.2  19.9  37.2  30.9  21.8"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gas[['GASEXP', 'GASP']].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10ba432d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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TWJcdO6c/mfiaunNtLL1unreH4Xek1eMBKvZRR1M1X1XbIFEhQVyuKPPKn79h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\naeEIMVvS4xde1TVo45vvVPG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       "text": [
        "<matplotlib.figure.Figure at 0x10ba20f50>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From Greene's [website](http://people.stern.nyu.edu/wgreene/Text/econometricanalysis.htm), we see have the following definitions.\n",
      "\n",
      "* Year = Year, 1953-2004,\n",
      "* GasExp = Total U.S. gasoline expenditure,\n",
      "* Pop = U.S. total population in thousands\n",
      "* GasP = Price index for gasoline,\n",
      "* Income = Per capita disposable income,\n",
      "* Pnc = Price index for new cars,\n",
      "* Puc = Price index for used cars,\n",
      "* Ppt = Price index for public transportation,\n",
      "* Pd = Aggregate price index for consumer durables,\n",
      "* Pn = Aggregate price index for consumer nondurables,\n",
      "* Ps = Aggregate price index for consumer services.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The model we're working with is\n",
      "\n",
      "\\begin{equation}\n",
      "    \\ln(G / pop) = \\beta_1 + \\beta_2 \\ln(Income / pop) + \\beta_3 \\ln price_G + \\beta_4 \\ln P_{newcars} + \\beta_5 \\ln P_{usedcars} + \\varepsilon\n",
      "\\end{equation}\n",
      "\n",
      "where $G$ is $GasExp$, $price_G$ is $GasP$, $P_{newcars}$ is $Pnc$, $P_{usedcars}$ is $Puc$, and the rest are from the table.  $\\epsilon$ is our random disturbance.\n",
      "\n",
      "First of all, we need to transform the data to logs."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "gas['ln_g_pc'] = np.log(gas.GASEXP / gas.POP)\n",
      "gas['ln_inc_pc'] = np.log(gas.INCOME / gas.POP)\n",
      "gas['ln_price_g'] = np.log(gas.GASP)\n",
      "gas['ln_p_nc'] = np.log(gas.PNC)\n",
      "gas['ln_p_uc'] = np.log(gas.PUC)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = smf.OLS.from_formula(formula='np.log(GASEXP / POP) ~ np.log(INCOME / POP) + '\n",
      "                             'np.log(GASP) + np.log(PNC) + np.log(PUC)', data=gas)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "res = model.fit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(res.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                             OLS Regression Results                             \n",
        "================================================================================\n",
        "Dep. Variable:     np.log(GASEXP / POP)   R-squared:                       0.995\n",
        "Model:                              OLS   Adj. R-squared:                  0.994\n",
        "Method:                   Least Squares   F-statistic:                     2252.\n",
        "Date:                  Wed, 17 Jul 2013   Prob (F-statistic):           4.94e-53\n",
        "Time:                          22:50:54   Log-Likelihood:                 69.511\n",
        "No. Observations:                    52   AIC:                            -129.0\n",
        "Df Residuals:                        47   BIC:                            -119.3\n",
        "Df Model:                             4                                         \n",
        "========================================================================================\n",
        "                           coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "----------------------------------------------------------------------------------------\n",
        "Intercept               -6.4445      0.649     -9.935      0.000        -7.749    -5.140\n",
        "np.log(INCOME / POP)     1.8085      0.186      9.718      0.000         1.434     2.183\n",
        "np.log(GASP)             0.9626      0.059     16.395      0.000         0.845     1.081\n",
        "np.log(PNC)             -0.2275      0.214     -1.064      0.293        -0.658     0.203\n",
        "np.log(PUC)              0.0264      0.139      0.190      0.850        -0.254     0.306\n",
        "==============================================================================\n",
        "Omnibus:                       17.346   Durbin-Watson:                   0.303\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.232\n",
        "Skew:                          -1.279   Prob(JB):                     2.45e-05\n",
        "Kurtosis:                       4.805   Cond. No.                         552.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The coefficient on the price of new cars is negative.  Is this puzzling?  See Greene's analysis for more."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figsize(14, 8)\n",
      "fig = sm.graphics.plot_regress_exog(res, exog_idx=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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15syZA8DVq1f5448/APDy8mLYsGFA1t9mAEuWLCEuLg4vLy9q165NREQEsbGxeHt7W+W1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\nV0+7Q9wY67rPtsBoUQh95JzHq/uxW5yrVKpmjffs2bORnp4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vYCDm2ynMFQoFevToIfz3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\nCasa56GhoYSGhlJUVMSZM2eoqamhb9++PPHEE/j4+Ng7RtEKe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NiwVldtlQVjlNdU8b73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       "text": [
        "<matplotlib.figure.Figure at 0x10bf0bb50>"
       ]
      }
     ],
     "prompt_number": 60
    }
   ],
   "metadata": {}
  }
 ]
}